SingleFITCLaplacianInferenceMethod.cpp

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/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (W) 2015 Wu Lin
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 *    list of conditions and the following disclaimer.
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 *    this list of conditions and the following disclaimer in the documentation
 *    and/or other materials provided with the distribution.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 * The views and conclusions contained in the software and documentation are those
 * of the authors and should not be interpreted as representing official policies,
 * either expressed or implied, of the Shogun Development Team.
 *
 */
#include <shogun/machine/gp/SingleFITCLaplacianInferenceMethod.h>

#ifdef HAVE_EIGEN3

#include <shogun/machine/gp/StudentsTLikelihood.h>
#include <shogun/mathematics/Math.h>
#include <shogun/lib/external/brent.h>
#include <shogun/mathematics/eigen3.h>
#include <shogun/features/DotFeatures.h>

using namespace shogun;
using namespace Eigen;

namespace shogun
{

#ifndef DOXYGEN_SHOULD_SKIP_THIS

class CFITCPsiLine : public func_base
{
public:
    float64_t log_scale;
    VectorXd dalpha;
    VectorXd start_alpha;
    SGVector<float64_t>* alpha;
    SGVector<float64_t>* dlp;
    SGVector<float64_t>* W;
    SGVector<float64_t>* f;
    SGVector<float64_t>* m;
    CLikelihoodModel* lik;
    CLabels* lab;
    CSingleFITCLaplacianInferenceMethod *inf;

    virtual double operator() (double x)
    {
        //time complexity O(m*n)
        Map<VectorXd> eigen_f(f->vector, f->vlen);
        Map<VectorXd> eigen_m(m->vector, m->vlen);
        Map<VectorXd> eigen_alpha(alpha->vector, alpha->vlen);

        //alpha = alpha + s*dalpha;
        eigen_alpha=start_alpha+x*dalpha;
        SGVector<float64_t> tmp=inf->compute_mvmK(*alpha);
        Map<VectorXd> eigen_tmp(tmp.vector, tmp.vlen);
        //f = mvmK(alpha,V,d0)+m;
        eigen_f=eigen_tmp+eigen_m;

        // get first and second derivatives of log likelihood
        (*dlp)=lik->get_log_probability_derivative_f(lab, (*f), 1);

        (*W)=lik->get_log_probability_derivative_f(lab, (*f), 2);
        W->scale(-1.0);

        // compute psi=alpha'*(f-m)/2-lp
        float64_t result = eigen_alpha.dot(eigen_f-eigen_m)/2.0-
            SGVector<float64_t>::sum(lik->get_log_probability_f(lab, *f));

        return result;
    }
};

#endif /* DOXYGEN_SHOULD_SKIP_THIS */

CSingleFITCLaplacianInferenceMethod::CSingleFITCLaplacianInferenceMethod() : CSingleFITCLaplacianBase()
{
    init();
}

CSingleFITCLaplacianInferenceMethod::CSingleFITCLaplacianInferenceMethod(CKernel* kern, CFeatures* feat,
        CMeanFunction* m, CLabels* lab, CLikelihoodModel* mod, CFeatures* lat)
        : CSingleFITCLaplacianBase(kern, feat, m, lab, mod, lat)
{
    init();
}

void CSingleFITCLaplacianInferenceMethod::init()
{
    m_iter=20;
    m_tolerance=1e-6;
    m_opt_tolerance=1e-10;
    m_opt_max=10;
    m_Psi=0;
    m_Wneg=false;

    SG_ADD(&m_dlp, "dlp", "derivative of log likelihood with respect to function location", MS_NOT_AVAILABLE);
    SG_ADD(&m_W, "W", "the noise matrix", MS_NOT_AVAILABLE);
    SG_ADD(&m_tolerance, "tolerance", "amount of tolerance for Newton's iterations", MS_NOT_AVAILABLE);
    SG_ADD(&m_iter, "iter", "max Newton's iterations", MS_NOT_AVAILABLE);
    SG_ADD(&m_opt_tolerance, "opt_tolerance", "amount of tolerance for Brent's minimization method", MS_NOT_AVAILABLE);
    SG_ADD(&m_opt_max, "opt_max", "max iterations for Brent's minimization method", MS_NOT_AVAILABLE);
    SG_ADD(&m_sW, "sW", "square root of W", MS_NOT_AVAILABLE);
    SG_ADD(&m_d2lp, "d2lp", "second derivative of log likelihood with respect to function location", MS_NOT_AVAILABLE);
    SG_ADD(&m_d3lp, "d3lp", "third derivative of log likelihood with respect to function location", MS_NOT_AVAILABLE);
    SG_ADD(&m_chol_R0, "chol_R0", "Cholesky of inverse covariance of inducing features", MS_NOT_AVAILABLE);
    SG_ADD(&m_dfhat, "dfhat", "derivative of negative log (approximated) marginal likelihood wrt f", MS_NOT_AVAILABLE);
    SG_ADD(&m_g, "g", "variable g defined in infFITC_Laplace.m", MS_NOT_AVAILABLE);
    SG_ADD(&m_dg, "dg", "variable d0 defined in infFITC_Laplace.m", MS_NOT_AVAILABLE);
    SG_ADD(&m_Psi, "Psi", "the negative log likelihood without constant terms used in Newton's method", MS_NOT_AVAILABLE);
    SG_ADD(&m_Wneg, "Wneg", "whether W contains negative elements", MS_NOT_AVAILABLE);
}

void CSingleFITCLaplacianInferenceMethod::compute_gradient()
{
    CInferenceMethod::compute_gradient();

    if (!m_gradient_update)
    {
        update_approx_cov();
        update_deriv();
        m_gradient_update=true;
        update_parameter_hash();
    }
}

void CSingleFITCLaplacianInferenceMethod::update()
{
    SG_DEBUG("entering\n");

    CInferenceMethod::update();
    update_init();
    update_alpha();
    update_chol();
    m_gradient_update=false;
    update_parameter_hash();

    SG_DEBUG("leaving\n");
}

SGVector<float64_t> CSingleFITCLaplacianInferenceMethod::get_diagonal_vector()
{
    if (parameter_hash_changed())
        update();

    return SGVector<float64_t>(m_sW);
}

CSingleFITCLaplacianInferenceMethod::~CSingleFITCLaplacianInferenceMethod()
{
}

SGVector<float64_t> CSingleFITCLaplacianInferenceMethod::compute_mvmZ(SGVector<float64_t> x)
{
    //time complexity O(m*n)
    Map<MatrixXd> eigen_Rvdd(m_Rvdd.matrix, m_Rvdd.num_rows, m_Rvdd.num_cols);
    Map<VectorXd> eigen_t(m_t.vector, m_t.vlen);
    Map<VectorXd> eigen_x(x.vector, x.vlen);

    SGVector<float64_t> res(x.vlen);
    Map<VectorXd> eigen_res(res.vector, res.vlen);

    //Zx = t.*x - RVdd'*(RVdd*x);
    eigen_res=eigen_x.cwiseProduct(eigen_t)-eigen_Rvdd.transpose()*(eigen_Rvdd*eigen_x);
    return res;
}

SGVector<float64_t> CSingleFITCLaplacianInferenceMethod::compute_mvmK(SGVector<float64_t> al)
{
    //time complexity O(m*n)
    Map<MatrixXd> eigen_V(m_V.matrix, m_V.num_rows, m_V.num_cols);
    Map<VectorXd> eigen_dg(m_dg.vector, m_dg.vlen);
    Map<VectorXd> eigen_al(al.vector, al.vlen);

    SGVector<float64_t> res(al.vlen);
    Map<VectorXd> eigen_res(res.vector, res.vlen);

    //Kal = V'*(V*al) + d0.*al;
    eigen_res= eigen_V.transpose()*(eigen_V*eigen_al)+eigen_dg.cwiseProduct(eigen_al);
    return res;
}

CSingleFITCLaplacianInferenceMethod* CSingleFITCLaplacianInferenceMethod::obtain_from_generic(
        CInferenceMethod* inference)
{
    REQUIRE(inference!=NULL, "Inference should be not NULL");

    if (inference->get_inference_type()!=INF_FITC_LAPLACIAN_SINGLE)
        SG_SERROR("Provided inference is not of type CSingleFITCLaplacianInferenceMethod!\n")

    SG_REF(inference);
    return (CSingleFITCLaplacianInferenceMethod*)inference;
}

SGMatrix<float64_t> CSingleFITCLaplacianInferenceMethod::get_chol_inv(SGMatrix<float64_t> mtx)
{
    //time complexity O(m^3), where mtx is a m-by-m matrix
    REQUIRE(mtx.num_rows==mtx.num_cols, "Matrix must be square\n");

    Map<MatrixXd> eigen_mtx(mtx.matrix, mtx.num_rows, mtx.num_cols);
    LLT<MatrixXd> chol(eigen_mtx.colwise().reverse().rowwise().reverse().matrix());
    //tmp=chol(rot180(A))'
    MatrixXd tmp=chol.matrixL();
    SGMatrix<float64_t> res(mtx.num_rows, mtx.num_cols);
    Map<MatrixXd> eigen_res(res.matrix, res.num_rows, res.num_cols);
    //chol_inv = @(A) rot180(chol(rot180(A))')\eye(nu);                 % chol(inv(A))
    eigen_res=tmp.colwise().reverse().rowwise().reverse().matrix().triangularView<Upper>(
        ).solve(MatrixXd::Identity(mtx.num_rows, mtx.num_cols));
    return res;
}

float64_t CSingleFITCLaplacianInferenceMethod::get_negative_log_marginal_likelihood()
{
    if (parameter_hash_changed())
        update();

    if (m_Wneg)
    {
        SG_WARNING("nlZ cannot be computed since W is too negative");
        //nlZ = NaN;
        return CMath::INFTY;
    }
    //time complexity O(m^2*n)
    Map<VectorXd> eigen_alpha(m_al.vector, m_al.vlen);
    Map<VectorXd> eigen_mu(m_mu.vector, m_mu.vlen);
    SGVector<float64_t> mean=m_mean->get_mean_vector(m_features);
    Map<VectorXd> eigen_mean(mean.vector, mean.vlen);
    // get log likelihood
    float64_t lp=SGVector<float64_t>::sum(m_model->get_log_probability_f(m_labels,
        m_mu));

    Map<MatrixXd> eigen_V(m_V.matrix, m_V.num_rows, m_V.num_cols);
    Map<VectorXd>eigen_t(m_t.vector, m_t.vlen);
    MatrixXd A=eigen_V*eigen_t.asDiagonal()*eigen_V.transpose()+MatrixXd::Identity(m_V.num_rows,m_V.num_rows);
    LLT<MatrixXd> chol(A);
    A=chol.matrixU();

    Map<VectorXd> eigen_dg(m_dg.vector, m_dg.vlen);
    Map<VectorXd> eigen_W(m_W.vector, m_W.vlen);

    //nlZ = alpha'*(f-m)/2 - sum(lp) - sum(log(dd))/2 + sum(log(diag(chol(A))));
    float64_t result=eigen_alpha.dot(eigen_mu-eigen_mean)/2.0-lp+
        A.diagonal().array().log().sum()+(eigen_W.cwiseProduct(eigen_dg)+MatrixXd::Ones(eigen_dg.rows(),1)).array().log().sum()/2.0;

    return result;
}

void CSingleFITCLaplacianInferenceMethod::update_approx_cov()
{
}

void CSingleFITCLaplacianInferenceMethod::update_init()
{
    //time complexity O(m^2*n)
    //m-by-m matrix
    Map<MatrixXd> eigen_kuu(m_kuu.matrix, m_kuu.num_rows, m_kuu.num_cols);
    //m-by-n matrix
    Map<MatrixXd> eigen_ktru(m_ktru.matrix, m_ktru.num_rows, m_ktru.num_cols);

    Map<VectorXd> eigen_ktrtr_diag(m_ktrtr_diag.vector, m_ktrtr_diag.vlen);

    SGMatrix<float64_t> cor_kuu(m_kuu.num_rows, m_kuu.num_cols);
    Map<MatrixXd> eigen_cor_kuu(cor_kuu.matrix, cor_kuu.num_rows, cor_kuu.num_cols);
    eigen_cor_kuu=eigen_kuu*CMath::exp(m_log_scale*2.0)+CMath::exp(m_log_ind_noise)*MatrixXd::Identity(
        m_kuu.num_rows, m_kuu.num_cols);
    //R0 = chol_inv(Kuu+snu2*eye(nu)); m-by-m matrix
    m_chol_R0=get_chol_inv(cor_kuu);
    Map<MatrixXd> eigen_R0(m_chol_R0.matrix, m_chol_R0.num_rows, m_chol_R0.num_cols);

    //V = R0*Ku;  m-by-n matrix
    m_V=SGMatrix<float64_t>(m_chol_R0.num_cols, m_ktru.num_cols);
    Map<MatrixXd> eigen_V(m_V.matrix, m_V.num_rows, m_V.num_cols);

    eigen_V=eigen_R0*(eigen_ktru*CMath::exp(m_log_scale*2.0));
    m_dg=SGVector<float64_t>(m_ktrtr_diag.vlen);
    Map<VectorXd> eigen_dg(m_dg.vector, m_dg.vlen);
    //d0 = diagK-sum(V.*V,1)';
    eigen_dg=eigen_ktrtr_diag*CMath::exp(m_log_scale*2.0)-(eigen_V.cwiseProduct(eigen_V)).colwise().sum().adjoint();

    // get mean vector and create eigen representation of it
    SGVector<float64_t> mean=m_mean->get_mean_vector(m_features);
    Map<VectorXd> eigen_mean(mean.vector, mean.vlen);

    // create shogun and eigen representation of function vector
    m_mu=SGVector<float64_t>(mean.vlen);
    Map<VectorXd> eigen_mu(m_mu, m_mu.vlen);

    float64_t Psi_New;
    float64_t Psi_Def;
    if (m_al.vlen!=m_labels->get_num_labels())
    {
        // set alpha a zero vector
        m_al=SGVector<float64_t>(m_labels->get_num_labels());
        m_al.zero();

        // f = mean, if length of alpha and length of y doesn't match
        eigen_mu=eigen_mean;

        Psi_New=-SGVector<float64_t>::sum(m_model->get_log_probability_f(
            m_labels, m_mu));
    }
    else
    {
        Map<VectorXd> eigen_alpha(m_al.vector, m_al.vlen);

        // compute f = K * alpha + m
        SGVector<float64_t> tmp=compute_mvmK(m_al);
        Map<VectorXd> eigen_tmp(tmp.vector, tmp.vlen);
        eigen_mu=eigen_tmp+eigen_mean;

        Psi_New=eigen_alpha.dot(eigen_tmp)/2.0-
            SGVector<float64_t>::sum(m_model->get_log_probability_f(m_labels, m_mu));

        Psi_Def=-SGVector<float64_t>::sum(m_model->get_log_probability_f(m_labels, mean));

        // if default is better, then use it
        if (Psi_Def < Psi_New)
        {
            m_al.zero();
            eigen_mu=eigen_mean;
            Psi_New=Psi_Def;
        }
    }
    m_Psi=Psi_New;
}

void CSingleFITCLaplacianInferenceMethod::update_alpha()
{
    //time complexity O(m^2*n)
    Map<MatrixXd> eigen_kuu(m_kuu.matrix, m_kuu.num_rows, m_kuu.num_cols);
    Map<MatrixXd> eigen_V(m_V.matrix, m_V.num_rows, m_V.num_cols);
    Map<VectorXd> eigen_dg(m_dg.vector, m_dg.vlen);
    Map<MatrixXd> eigen_R0(m_chol_R0.matrix, m_chol_R0.num_rows, m_chol_R0.num_cols);
    Map<VectorXd> eigen_mu(m_mu, m_mu.vlen);

    SGVector<float64_t> mean=m_mean->get_mean_vector(m_features);
    Map<VectorXd> eigen_mean(mean.vector, mean.vlen);

    float64_t Psi_Old=CMath::INFTY;
    float64_t Psi_New=m_Psi;

    // compute W = -d2lp
    m_W=m_model->get_log_probability_derivative_f(m_labels, m_mu, 2);
    m_W.scale(-1.0);

    //n-by-1 vector
    Map<VectorXd> eigen_al(m_al.vector, m_al.vlen);

    // get first derivative of log probability function
    m_dlp=m_model->get_log_probability_derivative_f(m_labels, m_mu, 1);

    index_t iter=0;

    m_Wneg=false;
    while (Psi_Old-Psi_New>m_tolerance && iter<m_iter)
    {
        //time complexity O(m^2*n)
        Map<VectorXd> eigen_W(m_W.vector, m_W.vlen);
        Map<VectorXd> eigen_dlp(m_dlp.vector, m_dlp.vlen);

        Psi_Old = Psi_New;
        iter++;

        if (eigen_W.minCoeff() < 0)
        {
            // Suggested by Vanhatalo et. al.,
            // Gaussian Process Regression with Student's t likelihood, NIPS 2009
            // Quoted from infFITC_Laplace.m
            float64_t df;

            if (m_model->get_model_type()==LT_STUDENTST)
            {
                CStudentsTLikelihood* lik=CStudentsTLikelihood::obtain_from_generic(m_model);
                df=lik->get_degrees_freedom();
                SG_UNREF(lik);
            }
            else
                df=1;
            eigen_W+=(2.0/(df+1))*eigen_dlp.cwiseProduct(eigen_dlp);
        }

        //b = W.*(f-m) + dlp;
        VectorXd b=eigen_W.cwiseProduct(eigen_mu-eigen_mean)+eigen_dlp;

        //dd = 1./(1+W.*d0);
        VectorXd dd=MatrixXd::Ones(b.rows(),1).cwiseQuotient(eigen_W.cwiseProduct(eigen_dg)+MatrixXd::Ones(b.rows(),1));

        VectorXd eigen_t=eigen_W.cwiseProduct(dd);
        //m-by-m matrix
        SGMatrix<float64_t> tmp(m_V.num_rows, m_V.num_rows);
        Map<MatrixXd> eigen_tmp(tmp.matrix, tmp.num_rows, tmp.num_cols);
        //eye(nu)+(V.*repmat((W.*dd)',nu,1))*V'
        eigen_tmp=eigen_V*eigen_t.asDiagonal()*eigen_V.transpose()+MatrixXd::Identity(tmp.num_rows,tmp.num_rows);
        tmp=get_chol_inv(tmp);
        //chol_inv(eye(nu)+(V.*repmat((W.*dd)',nu,1))*V')
        Map<MatrixXd> eigen_tmp2(tmp.matrix, tmp.num_rows, tmp.num_cols);
        //RV = chol_inv(eye(nu)+(V.*repmat((W.*dd)',nu,1))*V')*V;
        // m-by-n matrix
        MatrixXd eigen_RV=eigen_tmp2*eigen_V;
        //dalpha = dd.*b - (W.*dd).*(RV'*(RV*(dd.*b))) - alpha; % Newt dir + line search
        VectorXd dalpha=dd.cwiseProduct(b)-eigen_t.cwiseProduct(eigen_RV.transpose()*(eigen_RV*(dd.cwiseProduct(b))))-eigen_al;

        //perform Brent's optimization
        CFITCPsiLine func;

        func.log_scale=m_log_scale;
        func.dalpha=dalpha;
        func.start_alpha=eigen_al;
        func.alpha=&m_al;
        func.dlp=&m_dlp;
        func.f=&m_mu;
        func.m=&mean;
        func.W=&m_W;
        func.lik=m_model;
        func.lab=m_labels;
        func.inf=this;

        float64_t x;
        Psi_New=local_min(0, m_opt_max, m_opt_tolerance, func, x);
    }

    if (Psi_Old-Psi_New>m_tolerance && iter>=m_iter)
    {
        SG_WARNING("Max iterations (%d) reached, but convergence level (%f) is not yet below tolerance (%f)\n", m_iter, Psi_Old-Psi_New, m_tolerance);
    }

    // compute f = K * alpha + m
    SGVector<float64_t> tmp=compute_mvmK(m_al);
    Map<VectorXd> eigen_tmp(tmp.vector, tmp.vlen);
    eigen_mu=eigen_tmp+eigen_mean;

    m_alpha=SGVector<float64_t>(m_chol_R0.num_cols);
    Map<VectorXd> eigen_post_alpha(m_alpha.vector, m_alpha.vlen);
    //post.alpha = R0'*(V*alpha);
    //m-by-1 vector
    eigen_post_alpha=eigen_R0.transpose()*(eigen_V*eigen_al);
}

void CSingleFITCLaplacianInferenceMethod::update_chol()
{
    //time complexity O(m^2*n)
    Map<VectorXd> eigen_dg(m_dg.vector, m_dg.vlen);
    Map<MatrixXd> eigen_R0(m_chol_R0.matrix, m_chol_R0.num_rows, m_chol_R0.num_cols);
    Map<MatrixXd> eigen_V(m_V.matrix, m_V.num_rows, m_V.num_cols);

    // get log probability derivatives
    m_dlp=m_model->get_log_probability_derivative_f(m_labels, m_mu, 1);
    m_d2lp=m_model->get_log_probability_derivative_f(m_labels, m_mu, 2);
    m_d3lp=m_model->get_log_probability_derivative_f(m_labels, m_mu, 3);

    // W = -d2lp
    m_W=m_d2lp.clone();
    m_W.scale(-1.0);

    Map<VectorXd> eigen_W(m_W.vector, m_W.vlen);
    m_sW=SGVector<float64_t>(m_W.vlen);
    Map<VectorXd> eigen_sW(m_sW.vector, m_sW.vlen);

    VectorXd Wd0_1=eigen_W.cwiseProduct(eigen_dg)+MatrixXd::Ones(eigen_W.rows(),1);

    // compute sW
    // post.sW = sqrt(abs(W)).*sign(W);             % preserve sign in case of negative
    if (eigen_W.minCoeff()>0)
    {
        eigen_sW=eigen_W.cwiseSqrt();
    }
    else
    {
        eigen_sW=((eigen_W.array().abs()+eigen_W.array())/2).sqrt()-((eigen_W.array().abs()-eigen_W.array())/2).sqrt();
        //any(1+d0.*W<0)
        if (!(Wd0_1.array().abs().matrix()==Wd0_1))
            m_Wneg=true;
    }

    m_t=SGVector<float64_t>(m_W.vlen);
    Map<VectorXd>eigen_t(m_t.vector, m_t.vlen);

    //dd = 1./(1+d0.*W);
    VectorXd dd=MatrixXd::Ones(Wd0_1.rows(),1).cwiseQuotient(Wd0_1);
    eigen_t=eigen_W.cwiseProduct(dd);

    //m-by-m matrix
    SGMatrix<float64_t> A(m_V.num_rows, m_V.num_rows);
    Map<MatrixXd> eigen_A(A.matrix, A.num_rows, A.num_cols);
    //A = eye(nu)+(V.*repmat((W.*dd)',nu,1))*V';
    eigen_A=eigen_V*eigen_t.asDiagonal()*eigen_V.transpose()+MatrixXd::Identity(A.num_rows,A.num_rows);

    //R0tV = R0'*V; m-by-n
    MatrixXd R0tV=eigen_R0.transpose()*eigen_V;

    //B = R0tV.*repmat((W.*dd)',nu,1); m-by-n matrix
    MatrixXd B=R0tV*eigen_t.asDiagonal();

    //m-by-m matrix
    m_L=SGMatrix<float64_t>(m_kuu.num_rows, m_kuu.num_cols);
    Map<MatrixXd> eigen_L(m_L.matrix, m_L.num_rows, m_L.num_cols);

    //post.L = -B*R0tV';
    eigen_L=-B*R0tV.transpose();

    SGMatrix<float64_t> tmp=get_chol_inv(A);
    Map<MatrixXd> eigen_tmp(tmp.matrix, tmp.num_rows, tmp.num_cols);
    //RV = chol_inv(A)*V; m-by-n matrix
    MatrixXd eigen_RV=eigen_tmp*eigen_V;
    //RVdd m-by-n matrix
    m_Rvdd=SGMatrix<float64_t>(m_V.num_rows, m_V.num_cols);
    Map<MatrixXd> eigen_Rvdd(m_Rvdd.matrix, m_Rvdd.num_rows, m_Rvdd.num_cols);
    //RVdd = RV.*repmat((W.*dd)',nu,1);
    eigen_Rvdd=eigen_RV*eigen_t.asDiagonal();

    if (!m_Wneg)
    {
        //B = B*RV';
        B=B*eigen_RV.transpose();
        //post.L = post.L + B*B';
        eigen_L+=B*B.transpose();
    }
    else
    {
        //B = B*V';
        B=B*eigen_V.transpose();
        //post.L = post.L + (B*inv(A))*B';
        FullPivLU<MatrixXd> lu(eigen_A);
        eigen_L+=B*lu.inverse()*B.transpose();
    }

    Map<MatrixXd> eigen_ktru(m_ktru.matrix, m_ktru.num_rows, m_ktru.num_cols);
    m_g=SGVector<float64_t>(m_dg.vlen);
    Map<VectorXd> eigen_g(m_g.vector, m_g.vlen);
    //g = d/2 + sum(((R*R0)*P).^2,1)'/2
    eigen_g=((eigen_dg.cwiseProduct(dd)).array()+
        ((eigen_tmp*eigen_R0)*(eigen_ktru*CMath::exp(m_log_scale*2.0))*dd.asDiagonal()
         ).array().pow(2).colwise().sum().transpose())/2;
}

void CSingleFITCLaplacianInferenceMethod::update_deriv()
{
    //time complexity O(m^2*n)
    Map<MatrixXd> eigen_ktru(m_ktru.matrix, m_ktru.num_rows, m_ktru.num_cols);
    Map<MatrixXd> eigen_R0(m_chol_R0.matrix, m_chol_R0.num_rows, m_chol_R0.num_cols);
    Map<VectorXd> eigen_al(m_al.vector, m_al.vlen);
    Map<MatrixXd> eigen_V(m_V.matrix, m_V.num_rows, m_V.num_cols);
    // create shogun and eigen representation of B
    // m-by-n matrix
    m_B=SGMatrix<float64_t>(m_ktru.num_rows, m_ktru.num_cols);
    Map<MatrixXd> eigen_B(m_B.matrix, m_B.num_rows, m_B.num_cols);

    //B = (R0'*R0)*Ku
    eigen_B=eigen_R0.transpose()*eigen_V;

    // create shogun and eigen representation of w
    m_w=SGVector<float64_t>(m_B.num_rows);
    //w = B*al;
    Map<VectorXd> eigen_w(m_w.vector, m_w.vlen);
    eigen_w=eigen_B*eigen_al;

    // create shogun and eigen representation of the vector dfhat
    Map<VectorXd> eigen_d3lp(m_d3lp.vector, m_d3lp.vlen);
    Map<VectorXd> eigen_g(m_g.vector, m_g.vlen);
    m_dfhat=SGVector<float64_t>(m_g.vlen);
    Map<VectorXd> eigen_dfhat(m_dfhat.vector, m_dfhat.vlen);

    // compute derivative of nlZ wrt fhat
    // dfhat = g.*d3lp;
    eigen_dfhat=eigen_g.cwiseProduct(eigen_d3lp);
}

float64_t CSingleFITCLaplacianInferenceMethod::get_derivative_related_cov(SGVector<float64_t> ddiagKi,
    SGMatrix<float64_t> dKuui, SGMatrix<float64_t> dKui)
{
    //time complexity O(m^2*n)
    Map<MatrixXd> eigen_R0tV(m_B.matrix, m_B.num_rows, m_B.num_cols);
    Map<VectorXd> eigen_ddiagKi(ddiagKi.vector, ddiagKi.vlen);
    //m-by-m matrix
    Map<MatrixXd> eigen_dKuui(dKuui.matrix, dKuui.num_rows, dKuui.num_cols);
    //m-by-n matrix
    Map<MatrixXd> eigen_dKui(dKui.matrix, dKui.num_rows, dKui.num_cols);

    // compute R=2*dKui-dKuui*B
    SGMatrix<float64_t> dA(dKui.num_rows, dKui.num_cols);
    Map<MatrixXd> eigen_dA(dA.matrix, dA.num_rows, dA.num_cols);
    //dA = 2*dKu'-R0tV'*dKuu;
    //dA' = 2*dKu-dKuu'*R0tV;
    eigen_dA=2*eigen_dKui-eigen_dKuui*eigen_R0tV;

    SGVector<float64_t> v(ddiagKi.vlen);
    Map<VectorXd> eigen_v(v.vector, v.vlen);
    //w = sum(dA.*R0tV',2);
    //w' = sum(dA'.*R0tV,1);
    //v = ddiagK-w;
    eigen_v=eigen_ddiagKi-eigen_dA.cwiseProduct(eigen_R0tV).colwise().sum().transpose();

    //explicit term
    float64_t result=CSingleFITCLaplacianBase::get_derivative_related_cov(ddiagKi, dKuui, dKui, v, dA);

    //implicit term
    Map<VectorXd> eigen_dlp(m_dlp.vector, m_dlp.vlen);
    Map<VectorXd> eigen_dfhat(m_dfhat.vector, m_dfhat.vlen);

    SGVector<float64_t> b(v.vlen);
    Map<VectorXd> eigen_b(b.vector, b.vlen);
    //b = dA*(R0tV*dlp) + v.*dlp;
    eigen_b=eigen_dA.transpose()*(eigen_R0tV*eigen_dlp)+eigen_v.cwiseProduct(eigen_dlp);
    //KZb = mvmK(mvmZ(b,RVdd,t),V,d0);
    SGVector<float64_t> KZb=compute_mvmK(compute_mvmZ(b));
    Map<VectorXd> eigen_KZb(KZb.vector, KZb.vlen);
    //dnlZ.cov(i) = dnlZ.cov(i) - dfhat'*( b-KZb );
    result-=eigen_dfhat.dot(eigen_b-eigen_KZb);
    return result;
}

SGVector<float64_t> CSingleFITCLaplacianInferenceMethod::get_derivative_wrt_inference_method(
        const TParameter* param)
{
    REQUIRE(param, "Param not set\n");
    //time complexity O(m^2*n)
    REQUIRE(!(strcmp(param->m_name, "log_scale")
        && strcmp(param->m_name, "log_inducing_noise")
        && strcmp(param->m_name, "inducing_features")),
        "Can't compute derivative of"
        " the nagative log marginal likelihood wrt %s.%s parameter\n",
        get_name(), param->m_name)

    SGVector<float64_t> result;
    int32_t len;
    if (!strcmp(param->m_name, "inducing_features"))
    {
        if(m_Wneg)
        {
            int32_t dim=m_inducing_features.num_rows;
            int32_t num_samples=m_inducing_features.num_cols;
            len=dim*num_samples;
        }
        else if (!m_fully_sparse)
            return CSingleFITCLaplacianBase::get_derivative_wrt_inference_method(param);
        else
            return get_derivative_wrt_inducing_features(param);
    }
    else
        len=1;

    if (m_Wneg)
    {
        result=SGVector<float64_t>(len);
        return derivative_helper_when_Wneg(result, param);
    }

    if (!strcmp(param->m_name, "log_inducing_noise"))
        // wrt inducing_noise
        // compute derivative wrt inducing noise
        return get_derivative_wrt_inducing_noise(param);

    result=SGVector<float64_t>(len);
    // wrt scale
    // clone kernel matrices
    SGVector<float64_t> deriv_trtr=m_ktrtr_diag.clone();
    SGMatrix<float64_t> deriv_uu=m_kuu.clone();
    SGMatrix<float64_t> deriv_tru=m_ktru.clone();

    // create eigen representation of kernel matrices
    Map<VectorXd> ddiagKi(deriv_trtr.vector, deriv_trtr.vlen);
    Map<MatrixXd> dKuui(deriv_uu.matrix, deriv_uu.num_rows, deriv_uu.num_cols);
    Map<MatrixXd> dKui(deriv_tru.matrix, deriv_tru.num_rows, deriv_tru.num_cols);

    // compute derivatives wrt scale for each kernel matrix
    result[0]=get_derivative_related_cov(deriv_trtr, deriv_uu, deriv_tru);
    result[0]*=CMath::exp(m_log_scale*2.0)*2.0;
    return result;
}

SGVector<float64_t> CSingleFITCLaplacianInferenceMethod::get_derivative_wrt_likelihood_model(
        const TParameter* param)
{
    SGVector<float64_t> result(1);
    if (m_Wneg)
        return derivative_helper_when_Wneg(result, param);

    // get derivatives wrt likelihood model parameters
    SGVector<float64_t> lp_dhyp=m_model->get_first_derivative(m_labels,
            m_mu, param);
    SGVector<float64_t> dlp_dhyp=m_model->get_second_derivative(m_labels,
            m_mu, param);
    SGVector<float64_t> d2lp_dhyp=m_model->get_third_derivative(m_labels,
            m_mu, param);

    // create eigen representation of the derivatives
    Map<VectorXd> eigen_lp_dhyp(lp_dhyp.vector, lp_dhyp.vlen);
    Map<VectorXd> eigen_dlp_dhyp(dlp_dhyp.vector, dlp_dhyp.vlen);
    Map<VectorXd> eigen_d2lp_dhyp(d2lp_dhyp.vector, d2lp_dhyp.vlen);
    Map<VectorXd> eigen_g(m_g.vector, m_g.vlen);
    Map<VectorXd> eigen_dfhat(m_dfhat.vector, m_dfhat.vlen);

    //explicit term
    //dnlZ.lik(i) = -g'*d2lp_dhyp - sum(lp_dhyp);
    result[0]=-eigen_g.dot(eigen_d2lp_dhyp)-eigen_lp_dhyp.sum();

    //implicit term
    //b = mvmK(dlp_dhyp,V,d0);
    SGVector<float64_t> b=compute_mvmK(dlp_dhyp);
    //dnlZ.lik(i) = dnlZ.lik(i) - dfhat'*(b-mvmK(mvmZ(b,RVdd,t),V,d0));
    result[0]-= get_derivative_implicit_term_helper(b);

    return result;
}

SGVector<float64_t> CSingleFITCLaplacianInferenceMethod::get_derivative_wrt_kernel(
        const TParameter* param)
{
    REQUIRE(param, "Param not set\n");
    SGVector<float64_t> result;
    int64_t len=const_cast<TParameter *>(param)->m_datatype.get_num_elements();
    result=SGVector<float64_t>(len);

    if (m_Wneg)
        return derivative_helper_when_Wneg(result, param);

    m_lock->lock();
    CFeatures *inducing_features=get_inducing_features();
    for (index_t i=0; i<result.vlen; i++)
    {
        //time complexity O(m^2*n)
        SGVector<float64_t> deriv_trtr;
        SGMatrix<float64_t> deriv_uu;
        SGMatrix<float64_t> deriv_tru;

        m_kernel->init(m_features, m_features);
        deriv_trtr=m_kernel->get_parameter_gradient_diagonal(param, i);

        m_kernel->init(inducing_features, inducing_features);
        deriv_uu=m_kernel->get_parameter_gradient(param, i);

        m_kernel->init(inducing_features, m_features);
        deriv_tru=m_kernel->get_parameter_gradient(param, i);

        // create eigen representation of derivatives
        Map<VectorXd> ddiagKi(deriv_trtr.vector, deriv_trtr.vlen);
        Map<MatrixXd> dKuui(deriv_uu.matrix, deriv_uu.num_rows,
                deriv_uu.num_cols);
        Map<MatrixXd> dKui(deriv_tru.matrix, deriv_tru.num_rows,
                deriv_tru.num_cols);

        result[i]=get_derivative_related_cov(deriv_trtr, deriv_uu, deriv_tru);
        result[i]*=CMath::exp(m_log_scale*2.0);
    }
    SG_UNREF(inducing_features);
    m_lock->unlock();

    return result;
}

float64_t CSingleFITCLaplacianInferenceMethod::get_derivative_related_mean(SGVector<float64_t> dmu)
{
    //time complexity O(m*n)
    //explicit term
    float64_t result=CSingleFITCLaplacianBase::get_derivative_related_mean(dmu);

    //implicit term
    //Zdm = mvmZ(dm,RVdd,t);
    //tmp = mvmK(Zdm,V,d0)
    //dnlZ.mean(i) = dnlZ.mean(i) - dfhat'*(dm-mvmK(Zdm,V,d0));
    result-=get_derivative_implicit_term_helper(dmu);

    return result;
}

float64_t CSingleFITCLaplacianInferenceMethod::get_derivative_implicit_term_helper(SGVector<float64_t> d)
{
    //time complexity O(m*n)
    Map<VectorXd> eigen_d(d.vector, d.vlen);
    SGVector<float64_t> tmp=compute_mvmK(compute_mvmZ(d));
    Map<VectorXd> eigen_tmp(tmp.vector, tmp.vlen);
    Map<VectorXd> eigen_dfhat(m_dfhat.vector, m_dfhat.vlen);
    return eigen_dfhat.dot(eigen_d-eigen_tmp);
}

SGVector<float64_t> CSingleFITCLaplacianInferenceMethod::get_derivative_wrt_mean(
        const TParameter* param)
{
    //time complexity O(m*n)
    REQUIRE(param, "Param not set\n");
    SGVector<float64_t> result;
    int64_t len=const_cast<TParameter *>(param)->m_datatype.get_num_elements();
    result=SGVector<float64_t>(len);

    if (m_Wneg)
        return derivative_helper_when_Wneg(result, param);

    for (index_t i=0; i<result.vlen; i++)
    {
        SGVector<float64_t> dmu;
        dmu=m_mean->get_parameter_derivative(m_features, param, i);
        result[i]=get_derivative_related_mean(dmu);
    }

    return result;
}

SGVector<float64_t> CSingleFITCLaplacianInferenceMethod::derivative_helper_when_Wneg(
    SGVector<float64_t> res, const TParameter *param)
{
    REQUIRE(param, "Param not set\n");
    SG_WARNING("Derivative wrt %s cannot be computed since W (the Hessian (diagonal) matrix) is too negative\n", param->m_name);
    //dnlZ = struct('cov',0*hyp.cov, 'mean',0*hyp.mean, 'lik',0*hyp.lik);
    res.zero();
    return res;
}

SGVector<float64_t> CSingleFITCLaplacianInferenceMethod::get_derivative_wrt_inducing_features(
    const TParameter* param)
{
    //time complexity depends on the implementation of the provided kernel
    //time complexity is at least O(max((p*n*m),(m^2*n))), where p is the dimension (#) of features
    //For an ARD kernel with KL_FULL, the time complexity is O(max((p*n*m*d),(m^2*n)))
    //where the paramter \f$\Lambda\f$ of the ARD kerenl is a \f$d\f$-by-\f$p\f$ matrix,
    //For an ARD kernel with KL_SCALE and KL_DIAG, the time complexity is O(max((p*n*m),(m^2*n)))
    //efficiently compute the implicit term and explicit term at one shot
    Map<VectorXd> eigen_al(m_al.vector, m_al.vlen);
    Map<MatrixXd> eigen_Rvdd(m_Rvdd.matrix, m_Rvdd.num_rows, m_Rvdd.num_cols);
    //w=B*al
    Map<VectorXd> eigen_w(m_w.vector, m_w.vlen);
    Map<MatrixXd> eigen_B(m_B.matrix, m_B.num_rows, m_B.num_cols);
    Map<VectorXd> eigen_dlp(m_dlp.vector, m_dlp.vlen);
    Map<VectorXd> eigen_dfhat(m_dfhat.vector, m_dfhat.vlen);

    //q = dfhat - mvmZ(mvmK(dfhat,V,d0),RVdd,t);
    SGVector<float64_t> q=compute_mvmZ(compute_mvmK(m_dfhat));
    Map<VectorXd> eigen_q(q.vector, q.vlen);
    eigen_q=eigen_dfhat-eigen_q;

    //explicit term
    //diag_dK = alpha.*alpha + sum(RVdd.*RVdd,1)'-t, where t can be cancelled out
    //-v_1=get_derivative_related_cov_diagonal= -(alpha.*alpha + sum(RVdd.*RVdd,1)')
    //implicit term
    //-v_2=-2*dlp.*q
    //neg_v = -(diag_dK+ 2*dlp.*q);
    SGVector<float64_t> neg_v=get_derivative_related_cov_diagonal();
    Map<VectorXd> eigen_neg_v(neg_v.vector, neg_v.vlen);
    eigen_neg_v-=2*eigen_dlp.cwiseProduct(eigen_q);

    SGMatrix<float64_t> BdK(m_B.num_rows, m_B.num_cols);
    Map<MatrixXd> eigen_BdK(BdK.matrix, BdK.num_rows, BdK.num_cols);
    //explicit
    //BdK = (B*alpha)*alpha' + (B*RVdd')*RVdd - B.*repmat(v_1',nu,1),
    //implicit
    //BdK = BdK + (B*dlp)*q' + (B*q)*dlp' - B.*repmat(v_2',nu,1)
    //where v_1 is the explicit part of v and v_2 is the implicit part of v
    //v=v_1+v_2
    eigen_BdK=eigen_B*eigen_neg_v.asDiagonal()+eigen_w*(eigen_al.transpose())+
        (eigen_B*eigen_Rvdd.transpose())*eigen_Rvdd+
        (eigen_B*eigen_dlp)*eigen_q.transpose()+(eigen_B*eigen_q)*eigen_dlp.transpose();

    return get_derivative_related_inducing_features(BdK, param);
}

SGVector<float64_t> CSingleFITCLaplacianInferenceMethod::get_derivative_wrt_inducing_noise(
    const TParameter* param)
{
    //time complexity O(m^2*n)
    //explicit term
    SGVector<float64_t> result=CSingleFITCLaplacianBase::get_derivative_wrt_inducing_noise(param);

    //implicit term
    Map<MatrixXd> eigen_B(m_B.matrix, m_B.num_rows, m_B.num_cols);
    Map<VectorXd> eigen_dlp(m_dlp.vector, m_dlp.vlen);

    //snu = sqrt(snu2);
    //T = chol_inv(Kuu + snu2*eye(nu)); T = T'*(T*(snu*Ku));
    //t1 = sum(T.*T,1)';
    VectorXd eigen_t1=eigen_B.cwiseProduct(eigen_B).colwise().sum().adjoint();

    //b = (t1.*dlp-T'*(T*dlp))*2;
    SGVector<float64_t> b(eigen_t1.rows());
    Map<VectorXd> eigen_b(b.vector, b.vlen);
    float64_t factor=2.0*CMath::exp(m_log_ind_noise);
    eigen_b=(eigen_t1.cwiseProduct(eigen_dlp)-eigen_B.transpose()*(eigen_B*eigen_dlp))*factor;

    //KZb = mvmK(mvmZ(b,RVdd,t),V,d0);
    //z = z - dfhat'*( b-KZb );
    result[0]-=get_derivative_implicit_term_helper(b);

    return result;
}

SGVector<float64_t> CSingleFITCLaplacianInferenceMethod::get_posterior_mean()
{
    compute_gradient();

    SGVector<float64_t> res(m_mu.vlen);
    Map<VectorXd> eigen_res(res.vector, res.vlen);

    /*
    //true posterior mean with equivalent FITC prior approximated by Newton method
    //time complexity O(n)
    Map<VectorXd> eigen_mu(m_mu, m_mu.vlen);
    SGVector<float64_t> mean=m_mean->get_mean_vector(m_features);
    Map<VectorXd> eigen_mean(mean.vector, mean.vlen);
    eigen_res=eigen_mu-eigen_mean;
    */

    //FITC (further) approximated posterior mean with Netwon method
    //time complexity of the following operation is O(m*n)
    Map<VectorXd> eigen_post_alpha(m_alpha.vector, m_alpha.vlen);
    Map<MatrixXd> eigen_Ktru(m_ktru.matrix, m_ktru.num_rows, m_ktru.num_cols);
    eigen_res=CMath::exp(m_log_scale*2.0)*eigen_Ktru.adjoint()*eigen_post_alpha;

    return res;
}

SGMatrix<float64_t> CSingleFITCLaplacianInferenceMethod::get_posterior_covariance()
{
    compute_gradient();
    //time complexity of the following operations is O(m*n^2)
    //Warning: the the time complexity increases from O(m^2*n) to O(n^2*m) if this method is called
    m_Sigma=SGMatrix<float64_t>(m_ktrtr_diag.vlen, m_ktrtr_diag.vlen);
    Map<MatrixXd> eigen_Sigma(m_Sigma.matrix, m_Sigma.num_rows,
            m_Sigma.num_cols);

    //FITC (further) approximated posterior covariance with Netwon method
    Map<MatrixXd> eigen_L(m_L.matrix, m_L.num_rows, m_L.num_cols);
    Map<MatrixXd> eigen_Ktru(m_ktru.matrix, m_ktru.num_rows, m_ktru.num_cols);
    Map<VectorXd> eigen_dg(m_dg.vector, m_dg.vlen);
    Map<MatrixXd> eigen_V(m_V.matrix, m_V.num_rows, m_V.num_cols);

    MatrixXd diagonal_part=eigen_dg.asDiagonal();
    //FITC equivalent prior
    MatrixXd prior=eigen_V.transpose()*eigen_V+diagonal_part;

    MatrixXd tmp=CMath::exp(m_log_scale*2.0)*eigen_Ktru;
    eigen_Sigma=prior-tmp.adjoint()*eigen_L*tmp;

    /*
    //true posterior mean with equivalent FITC prior approximated by Newton method
    Map<VectorXd> eigen_t(m_t.vector, m_t.vlen);
    Map<MatrixXd> eigen_Rvdd(m_Rvdd.matrix, m_Rvdd.num_rows, m_Rvdd.num_cols);
    Map<VectorXd> eigen_W(m_W.vector, m_W.vlen);
    MatrixXd tmp1=eigen_Rvdd*prior;
    eigen_Sigma=prior+tmp.transpose()*tmp;

    MatrixXd tmp2=((eigen_dg.cwiseProduct(eigen_t)).asDiagonal()*eigen_V.transpose())*eigen_V;
    eigen_Sigma-=(tmp2+tmp2.transpose());
    eigen_Sigma-=eigen_V.transpose()*(eigen_V*eigen_t.asDiagonal()*eigen_V.transpose())*eigen_V;
    MatrixXd tmp3=((eigen_dg.cwiseProduct(eigen_dg)).cwiseProduct(eigen_t)).asDiagonal();
    eigen_Sigma-=tmp3;
    */

    return SGMatrix<float64_t>(m_Sigma);
}

}
#endif /* HAVE_EIGEN3 */